CN110739690A - Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility - Google Patents

Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility Download PDF

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CN110739690A
CN110739690A CN201911063390.6A CN201911063390A CN110739690A CN 110739690 A CN110739690 A CN 110739690A CN 201911063390 A CN201911063390 A CN 201911063390A CN 110739690 A CN110739690 A CN 110739690A
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distribution network
power distribution
energy storage
power
charging station
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王成福
刘晓艺
王宁
杨明
董晓明
王明强
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Shandong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

The invention discloses power distribution network optimal scheduling methods and systems considering electric vehicle quick charging station energy storage facilities, which reduce power distribution network operation cost and smooth power fluctuation of a power distribution network.

Description

Power distribution network optimal scheduling method and system considering electric vehicle quick charging station energy storage facility
Technical Field
The invention relates to the technical field of power distribution network optimization, in particular to power distribution network optimization scheduling methods and systems considering electric vehicle quick charging station energy storage facilities.
Background
In recent years, the electric vehicle production and sales volume in China is continuously increased and the electric vehicle is the first place in the world, the demand of an electric vehicle charging station is also increased, and the national power grid of the largest power system operator in China advocates a quick charging station (FCS) as a main type of a future public electric vehicle charging facility.
The charging station equipped with constant capacity energy storage system can not only suppress the fluctuation of the charging load, but also reduce the network loss of the power distribution network.
In order to quantify the potential of peak charging load reduction, the existing method comprises (1) providing a two-stage optimization model and performing cost-benefit analysis, (2) providing schemes for distributing power from a power grid to a charging station network containing an ESS and sequencing electric vehicle users, wherein the optimal scheduling model is established by considering the optimal scheduling model of a vehicle to the power grid (V2G) and (4) providing multi-cycle coordination functional-reactive scheduling models considering interactive load and battery storage energy to minimize power generation cost and carbon emission, and (5) discussing the value of an electric bus in CSEVs, considering not only reduction of network integration cost, but also reduction of cost of electric bus charging through electricity price.
The inventor finds that the above scheme has the following problems in the development process: when the energy storage external operation is considered, the cooperation of the ESS and the DG in the whole power distribution network operation is not considered; only the interaction of the load and the battery energy storage in a specific time period is considered, and the load change of the electric automobile in 24 hours is not considered; only the charging economic benefit of the electric automobile is considered, and the operation economy of the power distribution network with the quick charging station is not considered.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides power distribution network optimal scheduling methods and systems considering the energy storage facilities of the electric automobile quick charging station, so that the operation cost of the power distribution network is reduced, and the power fluctuation of the power distribution network is smoothed.
The invention provides a power distribution network optimal scheduling method considering an energy storage facility of an electric automobile quick charging station in the aspect of , which comprises the following technical scheme:
A power distribution network optimal scheduling method considering electric automobile quick charging station energy storage facilities comprises the following steps:
constructing a distribution network day scheduling model considering the charging load characteristics of the electric automobile;
carrying out linearization processing on the constructed day scheduling model of the power distribution network;
acquiring power distribution network load prediction data and electric vehicle load prediction data, inputting the power distribution network intraday scheduling model subjected to linearization processing, determining the charge-discharge period of the energy storage system, and evaluating the cost benefit.
, the method for constructing the intra-day scheduling model of the power distribution network comprises the following steps:
constructing an objective function for minimizing the operation cost of the power distribution network;
and setting constraint conditions and constructing a minimum objective function.
Further to step , the objective function is:
F＝CP+TB+OCES+OCDG+Ccurt
the minimum objective function is:
minF＝min(CP+TB+OCES+OCDG+Ccurt)
wherein CP is the cost of purchasing electricity from a distribution network substation, OCESAnd OCDGTotal running cost of ESS and DG days, TB total profit from selling electric power to the grid, CcurtIs the loss of benefit of DG curtailment.
And , the constraint conditions include a power balance constraint, a node voltage constraint, an out-of-branch capacity constraint, a distributed power output control constraint, a distributed energy storage system operation constraint and a radial network constraint.
, the method for performing linearization processing on the constructed day scheduling model of the power distribution network comprises the following steps:
linearizing the branch capacity limited constraint of the daily scheduling model of the power distribution network by adopting a polygonal internal approximation method to obtain the branch capacity limited secondary constraint of the daily scheduling model of the power distribution network;
and linearizing the secondary constraint of the branch capacity limitation by using a power condition formula.
Further , the power condition is formulated as:
Figure BDA0002256695500000031
wherein, Pij,t、Qij,tThe active power and the reactive power of the ij line at the moment t are obtained; gamma rayk0,γk1,γk2The coefficient corresponding to the linearized power circle constraint is changed along with the number of the divided edges of the regular polygon;
Figure BDA0002256695500000032
the maximum power allowed on branch ij.
The invention also provides a technical scheme of power distribution network optimal dispatching systems containing energy storage electric vehicle charging stations in the aspect of , which is as follows:
A power distribution network optimal dispatching system containing energy storage electric vehicle charging stations, the system includes:
the model construction module is used for constructing a distribution network day scheduling model considering the charging load characteristics of the electric automobile;
the model linearization processing module is used for carrying out linearization processing on the constructed day scheduling model of the power distribution network;
and the period determination module is used for acquiring power distribution network load prediction data and electric vehicle load prediction data, inputting the linearized power distribution network in-day scheduling model, determining the charge and discharge period of the energy storage system, and evaluating the cost benefit.
The computer-readable storage media provided by another aspect of the invention are:
computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for optimal scheduling of a power distribution network taking into account energy storage facilities at fast charging stations of electric vehicles as described above.
The invention also provides treatment devices in the aspect of , and the technical scheme is as follows:
processing device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to realize the steps of the method for optimizing and scheduling the power distribution network considering the energy storage facility of the electric automobile fast charging station.
Through the technical scheme, the invention has the beneficial effects that:
(1) according to the method, the minimum operation cost of the power distribution network is taken as a target function, the daily charging load requirement of the electric vehicle charging facility is considered to be dispatched into the power distribution network, and the time energy transfer characteristic and the time-of-use electricity price of the energy storage system are utilized, so that the operation economy of the power distribution network is improved.
(2) The optimized scheduling method provided by the invention considers the charging load demand of the electric automobile, and improves the node voltage grade in the power distribution network and reduces the network loss of the power distribution network by utilizing the fluctuation of the energy storage system of the electric automobile charging station to smooth the load of the power distribution network.
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The accompanying drawings, which form a part of the specification , are included to provide a further understanding of the invention, and are included to explain the present application and not to limit the invention.
FIG. 1 is a flowchart of an embodiment method for optimal scheduling of a power distribution network;
FIG. 2 is a schematic diagram of an inner polygonal approximation method in the example ;
FIG. 3 is a schematic diagram of an electrical distribution network of an IEEE 33 node including DGs and EVCSs in an embodiment ;
FIG. 4 is a graph of load, EV and DG predictions for example ;
FIG. 5 is a schematic diagram illustrating the scheduling results of the day-ahead charging and discharging cycle of ESS1 in example ;
FIG. 6 is a schematic diagram comparing the load of the network with and without stored energy according to example ;
FIG. 7 is a graph showing a comparison of the loss in the network in example ;
FIG. 8 is a graph of the voltage fluctuation of the node 18 in the example .
Detailed Description
The invention is further illustrated in with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further steps for the present invention unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The noun explains:
(1) ESS, distributed energy storage system;
(2) DG, distributed power supply.
Example
The embodiment provides power distribution network optimal scheduling methods considering electric vehicle quick charging station energy storage facilities, and relates to a power distribution network optimal scheduling method combining an electric vehicle charging station and an energy storage system.
Referring to fig. 1, the power distribution network optimal scheduling method considering the energy storage facility of the electric vehicle quick charging station includes the following steps:
s101, establishing a distribution network day scheduling model (OPF model) considering the charging load characteristics of the electric automobile.
In order to determine the model with the lowest operation cost, the specific implementation process of step 101 is as follows:
(1) defining an objective function
The objective function to minimize the operating cost of the distribution network can be expressed as:
F＝CP+TB+OCES+OCDG+Ccurt(1)
in the formula, CP is the cost of purchasing electricity from a distribution network substation, OCESAnd OCDGTotal running cost of ESS and DG days, TB total profit from selling electric power to the grid, CcurtIs the loss of benefit of DG curtailment. The expressions of the parameters of the objective function are respectively:
the cost CP of purchasing electricity from a substation in the distribution network is expressed as:
Figure BDA0002256695500000071
wherein, pitFor using time electricity prices, Pt fAnd purchasing power to the electric automobile of the transformer substation at the moment t, wherein the moment t is 1 hour.
The profit TB of the energy price arbitrage is expressed as:
Figure BDA0002256695500000072
in the formula, Pt ES,chAnd Pt ES,dcFor charging and discharging power of ESS at time t, i is network node. Energy prices of power distribution systems at different periods are different, so that the power distribution systems are required to respectively charge and discharge the ESS at low energy prices and high energy prices so as to ensure efficient dispatching of the ESS.
ESS operating cost OCESExpressed as:
Figure BDA0002256695500000073
operating cost OC of DGDGExpressed as:
Figure BDA0002256695500000074
wherein the content of the first and second substances,
Figure BDA0002256695500000075
and
Figure BDA0002256695500000076
respectively the running price parameters of the ESS and the DG; pt ES,maxAnd
Figure BDA0002256695500000077
the maximum power of ES and DG.
To reduce the cost of wind and light rejection, the loss of benefit of DG curtailment is expressed as:
Figure BDA0002256695500000078
wherein the content of the first and second substances,
Figure BDA0002256695500000079
a price parameter for the electricity discard amount for DG,
Figure BDA00022566955000000710
the predicted power for DG at time t and time i.
(2) Defining a minimum objective function
The objective function is given by equation (1), where F is the total cost and revenue per day plus the daily revenue of the distribution grid, and fmin is expressed as:
minF＝min(CP+TB+OCES+OCDG+Ccurt) (7)
the constraint is expressed as:
(a) and power balance constraint:
the power balance equation consists of a node active power balance equation, a node reactive power balance equation and a node voltage equation.
Uj,t＝Ui,t-(rijPij,t+xijQij,t) (10)
In the formula, Pij,t、Qij,tThe active power and the reactive power of the ij line at the moment t are obtained; load power of electric automobile
Figure BDA0002256695500000083
As a prediction parameter; charging and discharging power of charging station energy storage system
Figure BDA0002256695500000084
Participating in scheduling of the optimal power flow; u shapei.tIs the node voltage of bus i at time t.
(b) Node voltage Ui.tConstraining
Ui min≤Ui.t≤Ui max(11)
In this model, the root node voltage in the network is a reference value of 1, and the voltage on each node, except the root node, is allowed to fluctuate around a reference value of ± 7%. In the formula (11), the reaction mixture is,and
Figure BDA0002256695500000086
is a constraint on voltage.
(c) Branch capacity limitation t:
Figure BDA0002256695500000087
wherein S isij,maxIs the maximum capacity of the line.
(d) DG output control constraints
Figure BDA0002256695500000091
Figure BDA0002256695500000092
Figure BDA0002256695500000093
The output of the wind turbine and the photovoltaic can be adjusted within a fixed range of , the adjusting proportion is limited by the maximum allowable proportion of the cutting machine on the node, the proportion is set to be 20% in the embodiment, in addition, the model considers that the DG has reactive power adjusting capacity, and the potential of the DG participating in reactive power optimization can be exerted.
(e) ESS operation constraints:
Figure BDA0002256695500000094
Figure BDA0002256695500000096
Figure BDA0002256695500000097
Figure BDA0002256695500000098
wherein the content of the first and second substances,
Figure BDA0002256695500000099
and
Figure BDA00022566955000000910
to determine the 0-1 variable for charging and discharging the ESS; SOCe,tIndicating the state of charge of the ESS battery; mu.sc、μdRespectively, charge-discharge efficiency factor, mu, of the ESSc、μdSet to 0.9 and 1.11 in this model, respectively;
Figure BDA00022566955000000911
is the operational capacity limit of the ESS.
(f) Radial network constraints
Figure BDA00022566955000000912
In the formula, the left side of equal sign is the total number of closed branches of the scene s at the time t, NbusIs the total number of nodes, NfThe total number of the substations is, as the network reconfiguration is not considered in the embodiment, the network structure of the power distribution network remains unchanged in the operation process, and the power distribution network is always radial networks without a looped network.
And S102, carrying out linearization processing on the OPF model.
The OPF model described above is based on flow linear power flow the only non-linear term of is the equation (12), called the power circle constraint, it is clear that the feasible domains constrained by the constraint equation (12) are all within a circle, and the power circle constraint can be linearized using an approximation in a polygon, as shown in fig. 2.
The power circle quadratic constraint is then linearized with equation (22) and the power circle is replaced with an inner polygon.
Figure BDA0002256695500000101
Wherein, Pij,t、Qij,tFor the ij line at time tActive power and reactive power of; gamma rayk0,γk1,γk2The coefficient corresponding to the linearized power circle constraint is changed along with the number of the divided edges of the regular polygon;
Figure BDA0002256695500000102
the maximum power allowed on branch ij.
S103, obtaining branch load, node voltage, total load, 24-hour conventional load, electric vehicle load and 24-hour DG prediction data of the power distribution network, inputting a linear-processed power distribution network daily scheduling model, minimizing the operation cost of the power distribution network as a target function, determining the charge-discharge period of the energy storage system, and realizing optimized scheduling considering the electric vehicle quick charging station energy storage facility.
The power distribution network optimal scheduling method considering the electric vehicle quick charging station energy storage facility is verified below.
The embodiment establishes an improved IEEE-33 node power distribution network test system, as shown in FIG. 3, the total load size of the test system is 3715kW +2300kvar, wherein 3 DGs are provided, in the embodiment, the DGs are photovoltaic power generation (PV), the capacity of each DG is 200 kW., the load curve and the DG power prediction curve within 24 hours in days are shown in FIG. 4, three rapid charging stations comprising distributed Energy Storage Systems (ESS) are respectively installed at the node 5, the node 17 and the node 28, and the rapid charging stations can be solved through an MILP commercial solver such as CPLEX on a GAMS platform, and the parameters of the energy storage systems are shown in a table II.
In order to find an optimal scheduling method in the power distribution network, so that the operation cost is minimum, a daily load curve needs to be known. A typical daily load curve was calculated as shown in fig. 4.
From the historical time of use electricity rate information, electricity rates can be set as shown in table 1.
TABLE 1 Electricity prices at different time periods
Figure BDA0002256695500000111
TABLE 2 charging parameters for ESS
(1) Running cost/benefit analysis
The present embodiment evaluates the parameters in the cost-benefit model Eq. (1). The results are shown in Table 3. TB declined from 30126.07 to 29591.12. The energy storage access reduces the operation cost of the whole power distribution network by 1.78% through low-storage high-power generation, and has obvious economic benefit.
TABLE 3 cost-benefit comparison of energy storage with and without energy storage
(2) Electric automobile steady load curve analysis
Whether in an unregulated power market or in a regulated power environment, such as China, the consumer's retail electricity prices are fixed for a significant period of time .
Fig. 5 shows the scheduling results of the optimal charging and discharging scheme of the ESS1 according to equations (1) - (6). As can be seen from fig. 5, it is reasonable that the ESS1 is charged at lower prices and discharged at higher prices.
Fig. 6 is an analysis of the effect of integrated smoothing of the ESS on the load, it can be seen that the rapid charging station distributed energy storage system can perform a peak load valley filling action of , and the effect of smoothing the payload is shown in fig. 6.
(3) Analysis of reduced network losses and regulated voltage
Network loss after ESS integration is reduced by about 1.23%. As can be seen from FIG. 7, the grid loss is significantly reduced from 2770.48kW to 2736.34kW, which can improve the operating efficiency and profitability level of the power system.
As shown in fig. 8. The results indicate that in the case of ESS integration, the voltage at the end node of the network (e.g., node 18) is higher than it would be without the integrated ESS. In particular, at the worst case voltage, such as at time 20, the voltage can be effectively increased. Research shows that the integrated ESS can optimize power flow scheduling and improve voltage level.
The embodiment provides collaborative optimization scheduling methods of an FCS and an Energy Storage System (ESS), an optimization model is converted into an MILP problem based on a linearized power flow equation and a linearized method, then a commercial MILP solver such as CPLEX is used for solving on a GAMS platform, the effectiveness of the collaborative optimization scheduling method is verified on an IEEE-33 node distribution test system, the result shows that the distributed energy storage system can effectively inhibit the fluctuation of the power load, and when DESS exists, the strategy has good economic benefit, is beneficial to improving the node voltage level and reducing the network loss.
Example two
The embodiment provides kinds of distribution network optimal dispatching system who contains energy storage electric automobile charging station, and this system includes:
the model construction module is used for constructing a distribution network day scheduling model considering the charging load characteristics of the electric automobile;
the model linearization processing module is used for carrying out linearization processing on the constructed day scheduling model of the power distribution network;
and the period determining module is used for acquiring branch load, node voltage, total load, 24-hour conventional load, electric vehicle load and 24-hour DG prediction data of the power distribution network, inputting the linearized power distribution network intraday scheduling model and determining the charge and discharge period of the energy storage system.
EXAMPLE III
The present embodiment provides computer-readable storage media, on which a computer program is stored, which when executed by a processor implements the steps in the method for optimal scheduling of a power distribution network in consideration of energy storage facilities of an electric vehicle fast charging station as described above.
Example four
The present embodiment provides processing apparatuses, which include a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the steps of the method for optimal scheduling of the power distribution network in consideration of the energy storage facility of the electric vehicle fast charging station as described above.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1, A power distribution network optimal scheduling method considering electric automobile quick charging station energy storage facilities, which is characterized by comprising the following steps:
constructing a distribution network day scheduling model considering the charging load characteristics of the electric automobile;
carrying out linearization processing on the constructed day scheduling model of the power distribution network;
acquiring power distribution network load prediction data and electric vehicle load prediction data, inputting the power distribution network intraday scheduling model subjected to linearization processing, and determining the charge-discharge period of the energy storage system.
2. The optimal scheduling method of the power distribution network considering the energy storage facility of the electric automobile fast charging station as claimed in claim 1, wherein the construction method of the scheduling model in the day of the power distribution network is as follows:
constructing an objective function for minimizing the operation cost of the power distribution network;
and setting constraint conditions and constructing a minimum objective function.
3. The optimal scheduling method for the power distribution network considering the energy storage facility of the electric vehicle fast charging station as claimed in claim 2, wherein the objective function is as follows:
F＝CP+TB+OCES+OCDG+Ccurt
the minimum objective function is:
minF＝min(CP+TB+OCES+OCDG+Ccurt)
wherein CP is the cost of purchasing electricity from a distribution network substation, OCESAnd OCDGTotal running cost of ESS and DG days, TB total profit from selling electric power to the grid, CcurtIs the loss of benefit of DG curtailment.
4. The optimal scheduling method for the power distribution network considering the energy storage facilities of the electric vehicle fast charging station as claimed in claim 2, wherein the constraint conditions include power balance constraint, node voltage constraint, branch capacity violation, distributed power output control constraint, distributed energy storage system operation constraint and radial network constraint.
5. The optimal scheduling method of the power distribution network considering the energy storage facilities of the electric vehicle fast charging station as claimed in claim 1, wherein the method for performing linearization processing on the constructed day-to-day scheduling model of the power distribution network comprises the following steps:
linearizing the branch capacity limited constraint of the daily scheduling model of the power distribution network by adopting a polygonal internal approximation method to obtain the branch capacity limited secondary constraint of the daily scheduling model of the power distribution network;
and linearizing the secondary constraint of the branch capacity limitation by using a power condition formula.
6. The optimal scheduling method for the power distribution network considering the energy storage facility of the electric automobile fast charging station as claimed in claim 5, wherein the power condition formula is as follows:
Figure FDA0002256695490000021
wherein, Pij,t、Qij,tThe active power and the reactive power of the ij line at the moment t are obtained; gamma rayk0,γk1,γk2The coefficient corresponding to the linearized power circle constraint is changed along with the number of the divided edges of the regular polygon;
Figure FDA0002256695490000022
the maximum power allowed on branch ij.
7. The optimal scheduling method of the power distribution network considering the energy storage facility of the electric vehicle fast charging station as claimed in claim 1, wherein the power distribution network load prediction data comprises power distribution network branch load flow, node voltage, total load, and 24-hour conventional load data; the electric vehicle load prediction data comprises electric vehicle load and 24-hour DG prediction data.
8, kinds of distribution network optimization dispatch system who contains energy storage electric automobile charging station, characterized by includes:
the model construction module is used for constructing a distribution network day scheduling model considering the charging load characteristics of the electric automobile;
the model linearization processing module is used for carrying out linearization processing on the constructed day scheduling model of the power distribution network;
and the period determination module is used for acquiring the power distribution network load prediction data and the electric vehicle load prediction data, inputting the linearized power distribution network in-day scheduling model and determining the charge and discharge period of the energy storage system.
A computer-readable storage medium, on which a computer program is stored, wherein the program, when being executed by a processor, implements the steps of the method for optimal scheduling of a power distribution network considering energy storage facilities of an electric vehicle rapid charging station as claimed in any of claims 1-7.
10, processing apparatus, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for optimized scheduling of the power distribution network in consideration of the energy storage facility of the rapid charging station of the electric vehicle as claimed in any of claims 1 to 7.
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CN111723993A (en) * 2020-06-24 2020-09-29 南方电网科学研究院有限责任公司 Power distribution network double-layer cooperative scheduling method and device, terminal and storage medium
CN111723993B (en) * 2020-06-24 2024-04-26 南方电网科学研究院有限责任公司 Double-layer cooperative scheduling method, device, terminal and storage medium for power distribution network
CN112636363A (en) * 2020-12-29 2021-04-09 国网浙江省电力有限公司营销服务中心 Active and reactive distributed combined control method for electric vehicle charging station
CN112636363B (en) * 2020-12-29 2022-08-09 国网浙江省电力有限公司营销服务中心 Active and reactive distributed combined control method for electric vehicle charging station
CN113452052A (en) * 2021-06-30 2021-09-28 中国电力科学研究院有限公司 Dispatching method, system, equipment and storage medium for power distribution network containing charging and storage power station
CN116544920A (en) * 2023-05-09 2023-08-04 南京邮电大学 Residential area electric automobile night charging optimal control method, equipment and storage medium
CN116544920B (en) * 2023-05-09 2024-03-26 南京邮电大学 Residential area electric automobile night charging optimal control method, equipment and storage medium
CN117578488A (en) * 2023-11-15 2024-02-20 山东大学 Electric vehicle real-time flexibility assessment method and system considering uncertainty
CN117578488B (en) * 2023-11-15 2024-05-03 山东大学 Electric vehicle real-time flexibility assessment method and system considering uncertainty

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